0
$\begingroup$

I am interested in Maximal Information Coefficient (MIC) as an alternative to Pearson correlation when looking at gene co-expression from microarray data. I've read some very good posts on this website on MIC. However‚ they have mostly dealt with the theory. The Science paper is now quite a few years old. So I wondered what the practical experience with MIC is for microarray data. Is it fast enough for big datasets with many features such as microarrays? What about multiple testing? Doesn't the method detect too many co-expressed genes? Does it work equally well for tissue-specific versus housekeeping genes? (One of the strongest features of PCC when looking at multiple tissues is that it works mostly for tissue-specific genes.)

I would be grateful for practical hints and/or pointers to published papers where MIC was used for microarray data.

$\endgroup$
  • 1
    $\begingroup$ PS. Another common application for PCC when looking at microarrays is to look at expression divergence of duplicated genes over evolutionary time. Has anybody tried to apply MIC to this purpose? $\endgroup$ – Lukasz Huminiecki Jan 21 '19 at 5:27
0
$\begingroup$

You can read the mictools paper (https://doi.org/10.1093/gigascience/giy032). MICtools (https://github.com/minepy/mictools) is a practical, general purpose, open-source software for maximal information coefficient analysis. I think you can run it on your microarray data without problems. For multiple testing correction, MICtools makes available the strategies implemented in the Python Statsmodels package and a Python implementation of the Storey q-value method.

| cite | improve this answer | |
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.